{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-28T04:57:50.964475Z",
     "start_time": "2019-11-28T04:57:47.876990Z"
    }
   },
   "outputs": [],
   "source": [
    "#CW\n",
    "import torch\n",
    "import torchvision\n",
    "from torchvision import datasets, transforms\n",
    "from torch.autograd import Variable\n",
    "from torch.autograd.gradcheck import zero_gradients\n",
    "import torch.utils.data.dataloader as Data\n",
    "import torch.nn as nn\n",
    "from torchvision import models\n",
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-28T04:57:50.975085Z",
     "start_time": "2019-11-28T04:57:50.967218Z"
    }
   },
   "outputs": [],
   "source": [
    "#对比展现原始图片和对抗样本图片\n",
    "def show_images_diff(original_img,original_label,adversarial_img,adversarial_label):\n",
    "    plt.figure()\n",
    "\n",
    "    #归一化\n",
    "    if original_img.any() > 1.0:\n",
    "        original_img=original_img/255.0\n",
    "    if adversarial_img.any() > 1.0:\n",
    "        adversarial_img=adversarial_img/255.0\n",
    "\n",
    "    plt.subplot(131)\n",
    "    plt.title('Original')\n",
    "    plt.imshow(original_img)\n",
    "    plt.axis('off')\n",
    "\n",
    "    plt.subplot(132)\n",
    "    plt.title('Adversarial')\n",
    "    plt.imshow(adversarial_img)\n",
    "    plt.axis('off')\n",
    "\n",
    "    plt.subplot(133)\n",
    "    plt.title('Adversarial-Original')\n",
    "    difference = adversarial_img - original_img\n",
    "    #(-1,1)  -> (0,1)\n",
    "    difference=difference / abs(difference).max()/2.0+0.5\n",
    "    plt.imshow(difference,cmap=plt.cm.gray)\n",
    "    plt.axis('off')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-28T04:59:29.593165Z",
     "start_time": "2019-11-28T04:58:25.249567Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 3, 224, 224)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth\" to /home/shenchenkai/.cache/torch/checkpoints/alexnet-owt-4df8aa71.pth\n",
      "100%|██████████| 233M/233M [00:57<00:00, 4.25MB/s]   \n"
     ]
    }
   ],
   "source": [
    "#获取计算设备 默认是CPU\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "#图像加载以及预处理\n",
    "image_path=\"/home/shenchenkai/lab/adversarial_examples-master/picture/cropped_panda.jpg\"\n",
    "orig = cv2.imread(image_path)[..., ::-1]\n",
    "orig = cv2.resize(orig, (224, 224))\n",
    "img = orig.copy().astype(np.float32)\n",
    "\n",
    "mean = [0.485, 0.456, 0.406]\n",
    "std = [0.229, 0.224, 0.225]\n",
    "img /= 255.0\n",
    "img = (img - mean) / std\n",
    "img = img.transpose(2, 0, 1)\n",
    "\n",
    "img=np.expand_dims(img, axis=0)\n",
    "\n",
    "print(img.shape)\n",
    "\n",
    "#使用预测模式 主要影响droupout和BN层的行为\n",
    "model = models.alexnet(pretrained=True).to(device).eval()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-28T04:59:29.603743Z",
     "start_time": "2019-11-28T04:59:29.595567Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "#adam的最大迭代次数 论文中建议10000次 测试阶段1000也可以 1000次可以完成95%的优化工作\n",
    "max_iterations=1000\n",
    "#adam学习速率\n",
    "learning_rate=0.01\n",
    "#二分查找最大次数\n",
    "binary_search_steps=10\n",
    "#c的初始值\n",
    "initial_const=1e2\n",
    "confidence=initial_const\n",
    "\n",
    "#k值\n",
    "k=40\n",
    "\n",
    "#像素值区间\n",
    "boxmin = -3.0\n",
    "boxmax = 3.0\n",
    "\n",
    "#类别数 pytorch的实现里面是1000\n",
    "num_labels=1000\n",
    "\n",
    "#攻击目标标签 必须使用one hot编码\n",
    "target_label=288\n",
    "tlab=Variable(torch.from_numpy(np.eye(num_labels)[target_label]).to(device).float())\n",
    "\n",
    "\n",
    "print()\n",
    "\n",
    "shape = (1,3,224,224)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-28T04:59:30.489378Z",
     "start_time": "2019-11-28T04:59:30.483617Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "#c的初始化边界\n",
    "lower_bound = 0\n",
    "c=initial_const\n",
    "upper_bound = 1e10\n",
    "\n",
    "# the best l2, score, and image attack\n",
    "o_bestl2 = 1e10\n",
    "o_bestscore = -1\n",
    "o_bestattack = [np.zeros(shape)]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-28T05:01:38.010193Z",
     "start_time": "2019-11-28T04:59:58.994539Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "o_bestl2=10000000000.0 confidence=100.0\n",
      "attack success l2=24.11794090270996 target_label=288\n",
      "iteration=100 loss=125.19587707519531 loss1=0.0 loss2=125.19587707519531\n",
      "iteration=200 loss=116.37782287597656 loss1=0.0 loss2=116.37782287597656\n",
      "iteration=300 loss=440.47747802734375 loss1=333.6418151855469 loss2=106.83567810058594\n",
      "iteration=400 loss=131.62060546875 loss1=0.0 loss2=131.62060546875\n",
      "iteration=500 loss=121.77427673339844 loss1=0.0 loss2=121.77427673339844\n",
      "iteration=600 loss=107.68809509277344 loss1=0.0 loss2=107.68809509277344\n",
      "iteration=700 loss=138.96188354492188 loss1=0.0 loss2=138.96188354492188\n",
      "iteration=800 loss=123.73726654052734 loss1=0.0 loss2=123.73726654052734\n",
      "iteration=900 loss=107.75611877441406 loss1=0.0 loss2=107.75611877441406\n",
      "iteration=1000 loss=132.7791748046875 loss1=0.0 loss2=132.7791748046875\n",
      "\n",
      "outer_step=0 confidence 100.0->50.0\n",
      "o_bestl2=24.11794090270996 confidence=50.0\n",
      "attack success l2=24.034883499145508 target_label=288\n",
      "iteration=100 loss=117.7539291381836 loss1=0.0 loss2=117.7539291381836\n",
      "iteration=200 loss=105.56501007080078 loss1=0.0 loss2=105.56501007080078\n",
      "iteration=300 loss=105.39325714111328 loss1=0.0 loss2=105.39325714111328\n",
      "iteration=400 loss=116.68194580078125 loss1=0.0 loss2=116.68194580078125\n",
      "iteration=500 loss=97.46041107177734 loss1=0.0 loss2=97.46041107177734\n",
      "iteration=600 loss=125.20096588134766 loss1=0.0 loss2=125.20096588134766\n",
      "iteration=700 loss=107.56014251708984 loss1=0.0 loss2=107.56014251708984\n",
      "iteration=800 loss=105.57412719726562 loss1=0.0 loss2=105.57412719726562\n",
      "iteration=900 loss=117.71306610107422 loss1=0.0 loss2=117.71306610107422\n",
      "iteration=1000 loss=126.80354309082031 loss1=0.0 loss2=126.80354309082031\n",
      "\n",
      "outer_step=1 confidence 50.0->25.0\n",
      "o_bestl2=24.034883499145508 confidence=25.0\n",
      "attack success l2=23.87195587158203 target_label=288\n",
      "iteration=100 loss=110.74977111816406 loss1=0.0 loss2=110.74977111816406\n",
      "iteration=200 loss=97.6969985961914 loss1=0.0 loss2=97.6969985961914\n",
      "iteration=300 loss=96.94530487060547 loss1=0.0 loss2=96.94530487060547\n",
      "iteration=400 loss=91.60255432128906 loss1=0.0 loss2=91.60255432128906\n",
      "iteration=500 loss=98.60176086425781 loss1=0.0 loss2=98.60176086425781\n",
      "iteration=600 loss=99.4427490234375 loss1=0.0 loss2=99.4427490234375\n",
      "iteration=700 loss=100.7470474243164 loss1=0.0 loss2=100.7470474243164\n",
      "iteration=800 loss=86.26828002929688 loss1=0.0 loss2=86.26828002929688\n",
      "iteration=900 loss=93.98387145996094 loss1=0.0 loss2=93.98387145996094\n",
      "iteration=1000 loss=94.46053314208984 loss1=0.0 loss2=94.46053314208984\n",
      "\n",
      "outer_step=2 confidence 25.0->12.5\n",
      "o_bestl2=23.87195587158203 confidence=12.5\n",
      "attack success l2=23.544822692871094 target_label=288\n",
      "iteration=100 loss=96.7177505493164 loss1=0.0 loss2=96.7177505493164\n",
      "iteration=200 loss=79.40478515625 loss1=0.0 loss2=79.40478515625\n",
      "iteration=300 loss=90.28797149658203 loss1=0.0 loss2=90.28797149658203\n",
      "iteration=400 loss=81.20881652832031 loss1=0.0 loss2=81.20881652832031\n",
      "iteration=500 loss=79.76606750488281 loss1=0.0 loss2=79.76606750488281\n",
      "iteration=600 loss=71.74378204345703 loss1=0.0 loss2=71.74378204345703\n",
      "iteration=700 loss=81.02471923828125 loss1=0.0 loss2=81.02471923828125\n",
      "iteration=800 loss=77.8258056640625 loss1=0.0 loss2=77.8258056640625\n",
      "iteration=900 loss=70.75613403320312 loss1=0.0 loss2=70.75613403320312\n",
      "iteration=1000 loss=76.10968780517578 loss1=0.0 loss2=76.10968780517578\n",
      "\n",
      "outer_step=3 confidence 12.5->6.25\n",
      "o_bestl2=23.544822692871094 confidence=6.25\n",
      "attack success l2=22.907657623291016 target_label=288\n",
      "iteration=100 loss=87.65644073486328 loss1=0.10182857513427734 loss2=87.55461120605469\n",
      "iteration=200 loss=72.65103149414062 loss1=0.0 loss2=72.65103149414062\n",
      "iteration=300 loss=62.55534362792969 loss1=0.0 loss2=62.55534362792969\n",
      "iteration=400 loss=63.25142288208008 loss1=0.0 loss2=63.25142288208008\n",
      "iteration=500 loss=71.81632232666016 loss1=0.0 loss2=71.81632232666016\n",
      "iteration=600 loss=60.485862731933594 loss1=0.0 loss2=60.485862731933594\n",
      "iteration=700 loss=61.90065002441406 loss1=0.0 loss2=61.90065002441406\n",
      "iteration=800 loss=63.3380012512207 loss1=0.0 loss2=63.3380012512207\n",
      "iteration=900 loss=57.31077194213867 loss1=0.0 loss2=57.31077194213867\n",
      "iteration=1000 loss=58.62847137451172 loss1=0.0 loss2=58.62847137451172\n",
      "\n",
      "outer_step=4 confidence 6.25->3.125\n",
      "o_bestl2=22.907657623291016 confidence=3.125\n",
      "attack success l2=21.65084457397461 target_label=288\n",
      "iteration=100 loss=66.33782958984375 loss1=0.0 loss2=66.33782958984375\n",
      "iteration=200 loss=55.28645324707031 loss1=0.0 loss2=55.28645324707031\n",
      "iteration=300 loss=49.56264877319336 loss1=0.0 loss2=49.56264877319336\n",
      "iteration=400 loss=53.49805450439453 loss1=3.6916255950927734 loss2=49.80643081665039\n",
      "iteration=500 loss=49.200355529785156 loss1=0.0 loss2=49.200355529785156\n",
      "iteration=600 loss=49.89018630981445 loss1=0.0 loss2=49.89018630981445\n",
      "iteration=700 loss=48.56795883178711 loss1=0.0 loss2=48.56795883178711\n",
      "iteration=800 loss=50.110443115234375 loss1=3.6199092864990234 loss2=46.490535736083984\n",
      "iteration=900 loss=48.49361801147461 loss1=0.0 loss2=48.49361801147461\n",
      "iteration=1000 loss=48.693115234375 loss1=0.0 loss2=48.693115234375\n",
      "\n",
      "outer_step=5 confidence 3.125->1.5625\n",
      "o_bestl2=21.65084457397461 confidence=1.5625\n",
      "attack success l2=19.302310943603516 target_label=288\n",
      "attack success l2=19.011207580566406 target_label=288\n",
      "attack success l2=18.808204650878906 target_label=288\n",
      "attack success l2=18.625551223754883 target_label=288\n",
      "attack success l2=18.475849151611328 target_label=288\n",
      "attack success l2=18.333097457885742 target_label=288\n",
      "attack success l2=18.223722457885742 target_label=288\n",
      "attack success l2=18.121620178222656 target_label=288\n",
      "attack success l2=18.039276123046875 target_label=288\n",
      "attack success l2=17.949987411499023 target_label=288\n",
      "attack success l2=17.888792037963867 target_label=288\n",
      "attack success l2=17.797067642211914 target_label=288\n",
      "attack success l2=17.730852127075195 target_label=288\n",
      "attack success l2=17.646711349487305 target_label=288\n",
      "attack success l2=17.63106346130371 target_label=288\n",
      "attack success l2=17.6037654876709 target_label=288\n",
      "attack success l2=17.584810256958008 target_label=288\n",
      "iteration=100 loss=61.551326751708984 loss1=32.76446533203125 loss2=28.786861419677734\n",
      "iteration=200 loss=56.282474517822266 loss1=15.442824363708496 loss2=40.83964920043945\n",
      "iteration=300 loss=54.6205940246582 loss1=7.265800476074219 loss2=47.354793548583984\n",
      "iteration=400 loss=52.8281135559082 loss1=2.274113893508911 loss2=50.55400085449219\n",
      "iteration=500 loss=52.074134826660156 loss1=0.677567720413208 loss2=51.396568298339844\n",
      "iteration=600 loss=51.48061752319336 loss1=1.1137604713439941 loss2=50.36685562133789\n",
      "iteration=700 loss=50.761173248291016 loss1=1.9713819026947021 loss2=48.789791107177734\n",
      "iteration=800 loss=50.64321517944336 loss1=0.7179081439971924 loss2=49.92530822753906\n",
      "iteration=900 loss=49.024452209472656 loss1=0.8598983287811279 loss2=48.164554595947266\n",
      "iteration=1000 loss=50.5410041809082 loss1=1.767486333847046 loss2=48.77351760864258\n",
      "\n",
      "outer_step=6 confidence 1.5625->0.78125\n",
      "o_bestl2=17.584810256958008 confidence=0.78125\n",
      "attack success l2=15.213457107543945 target_label=288\n",
      "attack success l2=14.96298599243164 target_label=288\n",
      "attack success l2=14.487607955932617 target_label=288\n",
      "attack success l2=13.869768142700195 target_label=288\n",
      "attack success l2=13.066222190856934 target_label=288\n",
      "attack success l2=12.190964698791504 target_label=288\n",
      "attack success l2=11.345080375671387 target_label=288\n",
      "attack success l2=10.597637176513672 target_label=288\n",
      "attack success l2=9.90034008026123 target_label=288\n",
      "attack success l2=9.888551712036133 target_label=288\n",
      "attack success l2=9.872919082641602 target_label=288\n",
      "attack success l2=9.831876754760742 target_label=288\n",
      "attack success l2=9.76981258392334 target_label=288\n",
      "attack success l2=9.607747077941895 target_label=288\n",
      "attack success l2=9.481654167175293 target_label=288\n",
      "attack success l2=9.410765647888184 target_label=288\n",
      "attack success l2=9.331978797912598 target_label=288\n",
      "attack success l2=9.263164520263672 target_label=288\n",
      "attack success l2=9.193808555603027 target_label=288\n",
      "iteration=100 loss=36.97016143798828 loss1=26.863521575927734 loss2=10.106640815734863\n",
      "iteration=200 loss=38.43123245239258 loss1=27.2705020904541 loss2=11.160731315612793\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration=300 loss=36.672142028808594 loss1=26.040761947631836 loss2=10.631381034851074\n",
      "iteration=400 loss=36.28478240966797 loss1=25.85200309753418 loss2=10.432780265808105\n",
      "iteration=500 loss=37.20997619628906 loss1=26.05133056640625 loss2=11.158647537231445\n",
      "iteration=600 loss=38.384403228759766 loss1=27.151206970214844 loss2=11.233196258544922\n",
      "iteration=700 loss=36.96193313598633 loss1=26.19225311279297 loss2=10.76968002319336\n",
      "iteration=800 loss=37.648250579833984 loss1=26.475095748901367 loss2=11.173154830932617\n",
      "iteration=900 loss=36.970619201660156 loss1=25.853923797607422 loss2=11.11669635772705\n",
      "iteration=1000 loss=38.790618896484375 loss1=26.649093627929688 loss2=12.141525268554688\n",
      "\n",
      "outer_step=7 confidence 0.78125->0.390625\n",
      "o_bestl2=9.193808555603027 confidence=0.390625\n",
      "attack success l2=9.088075637817383 target_label=288\n",
      "attack success l2=8.085453987121582 target_label=288\n",
      "attack success l2=7.935295104980469 target_label=288\n",
      "attack success l2=7.856208324432373 target_label=288\n",
      "attack success l2=7.440967559814453 target_label=288\n",
      "attack success l2=7.149928569793701 target_label=288\n",
      "attack success l2=6.889289379119873 target_label=288\n",
      "attack success l2=6.581423759460449 target_label=288\n",
      "attack success l2=6.243889331817627 target_label=288\n",
      "attack success l2=6.1226935386657715 target_label=288\n",
      "attack success l2=5.866607666015625 target_label=288\n",
      "attack success l2=5.815626621246338 target_label=288\n",
      "attack success l2=5.589850425720215 target_label=288\n",
      "attack success l2=5.499844074249268 target_label=288\n",
      "attack success l2=5.333562850952148 target_label=288\n",
      "attack success l2=5.247480869293213 target_label=288\n",
      "attack success l2=5.221987247467041 target_label=288\n",
      "iteration=100 loss=20.56146240234375 loss1=14.946624755859375 loss2=5.614838600158691\n",
      "attack success l2=5.191378116607666 target_label=288\n",
      "iteration=200 loss=20.87276840209961 loss1=15.064626693725586 loss2=5.80814266204834\n",
      "iteration=300 loss=20.267620086669922 loss1=15.054805755615234 loss2=5.212815284729004\n",
      "attack success l2=5.0834574699401855 target_label=288\n",
      "attack success l2=4.991557598114014 target_label=288\n",
      "iteration=400 loss=20.82540512084961 loss1=15.04188060760498 loss2=5.783524990081787\n",
      "iteration=500 loss=20.70514678955078 loss1=14.94925594329834 loss2=5.755890846252441\n",
      "iteration=600 loss=20.731168746948242 loss1=14.978726387023926 loss2=5.752442836761475\n",
      "iteration=700 loss=20.670549392700195 loss1=15.673735618591309 loss2=4.996814250946045\n",
      "attack success l2=4.981553554534912 target_label=288\n",
      "iteration=800 loss=20.80792236328125 loss1=15.431920051574707 loss2=5.376001358032227\n",
      "iteration=900 loss=20.460201263427734 loss1=14.854937553405762 loss2=5.605263710021973\n",
      "iteration=1000 loss=21.015480041503906 loss1=14.839130401611328 loss2=6.1763505935668945\n",
      "\n",
      "outer_step=8 confidence 0.390625->0.1953125\n",
      "o_bestl2=4.981553554534912 confidence=0.1953125\n",
      "attack success l2=4.5926833152771 target_label=288\n",
      "iteration=100 loss=10.635278701782227 loss1=9.433502197265625 loss2=1.2017762660980225\n",
      "iteration=200 loss=10.564374923706055 loss1=9.824612617492676 loss2=0.7397626042366028\n",
      "iteration=300 loss=10.570599555969238 loss1=9.753324508666992 loss2=0.8172752261161804\n",
      "iteration=400 loss=10.572216033935547 loss1=9.78063678741455 loss2=0.7915796637535095\n",
      "iteration=500 loss=10.586817741394043 loss1=9.883360862731934 loss2=0.7034568786621094\n",
      "iteration=600 loss=10.596470832824707 loss1=9.71792221069336 loss2=0.8785490393638611\n",
      "iteration=700 loss=10.57168197631836 loss1=9.745476722717285 loss2=0.8262051939964294\n",
      "iteration=800 loss=10.57372760772705 loss1=9.689743041992188 loss2=0.8839847445487976\n",
      "iteration=900 loss=10.597302436828613 loss1=9.704747200012207 loss2=0.8925550580024719\n",
      "iteration=1000 loss=10.574882507324219 loss1=9.826601028442383 loss2=0.748281717300415\n",
      "\n",
      "outer_step=9 confidence 0.1953125->0.09765625\n"
     ]
    }
   ],
   "source": [
    "# the resulting image, tanh'd to keep bounded from boxmin to boxmax\n",
    "boxmul = (boxmax - boxmin) / 2.\n",
    "boxplus = (boxmin + boxmax) / 2.\n",
    "\n",
    "for outer_step in range(binary_search_steps):\n",
    "    print(\"o_bestl2={} confidence={}\".format(o_bestl2,confidence)  )\n",
    "    \n",
    "    #把原始图像转换成图像数据和扰动的形态\n",
    "    timg = Variable(torch.from_numpy(np.arctanh((img - boxplus) / boxmul * 0.999999)).to(device).float())\n",
    "    modifier=Variable(torch.zeros_like(timg).to(device).float())\n",
    "    \n",
    "\n",
    "    #设置为不保存梯度值 自然也无法修改\n",
    "    #for param in model.parameters():\n",
    "    #    param.requires_grad = False\n",
    "        \n",
    "    #图像数据的扰动量梯度可以获取\n",
    "    modifier.requires_grad = True\n",
    "    \n",
    "\n",
    "    #定义优化器 仅优化modifier\n",
    "    optimizer = torch.optim.Adam([modifier],lr=learning_rate)\n",
    "    \n",
    "    for iteration in range(1,max_iterations+1):\n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        #定义新输入\n",
    "        newimg = torch.tanh(modifier + timg) * boxmul + boxplus\n",
    "      \n",
    "        output=model(newimg)\n",
    "             \n",
    "        #定义cw中的损失函数\n",
    "        \n",
    "        #l2范数\n",
    "        #l2dist = tf.reduce_sum(tf.square(newimg-(tf.tanh(timg) * boxmul + boxplus)),[1,2,3])\n",
    "        #loss2 = tf.reduce_sum(l2dist)\n",
    "        loss2=torch.dist(newimg,(torch.tanh(timg) * boxmul + boxplus),p=2)\n",
    "        \n",
    "        \"\"\"\n",
    "        # compute the probability of the label class versus the maximum other\n",
    "            real = tf.reduce_sum((tlab)*output,1)\n",
    "            # 论文中的开源实现 other = tf.reduce_max((1-tlab)*output - (tlab*10000),1)\n",
    "            other = tf.reduce_max((1-tlab)*output)\n",
    "            loss1 = tf.maximum(0.0, other-real+k)\n",
    "            loss1 = tf.reduce_sum(const*loss1)\n",
    "        \"\"\"\n",
    "               \n",
    "        real=torch.max(output*tlab)\n",
    "        other=torch.max((1-tlab)*output)  \n",
    "        loss1=other-real+k   \n",
    "        loss1=torch.clamp(loss1,min=0)\n",
    "             \n",
    "        loss1=confidence*loss1\n",
    "           \n",
    "        loss=loss1+loss2\n",
    "        \n",
    "            \n",
    "        loss.backward(retain_graph=True)\n",
    "\n",
    "        optimizer.step()\n",
    "              \n",
    "        l2=loss2\n",
    "        \n",
    "        sc=output.data.cpu().numpy()\n",
    "        \n",
    "        # print out the losses every 10%\n",
    "        if iteration%(max_iterations//10) == 0:\n",
    "            print(\"iteration={} loss={} loss1={} loss2={}\".format(iteration,loss,loss1,loss2))\n",
    "              \n",
    "        if (l2 < o_bestl2) and (np.argmax(sc) == target_label  ):\n",
    "            print(\"attack success l2={} target_label={}\".format(l2,target_label))\n",
    "            o_bestl2 = l2\n",
    "            o_bestscore = np.argmax(sc)\n",
    "            o_bestattack = newimg.data.cpu().numpy()\n",
    "            \n",
    "    confidence_old=-1       \n",
    "    if (o_bestscore == target_label) and o_bestscore != -1:\n",
    "        #攻击成功 减小c\n",
    "        upper_bound = min(upper_bound,confidence)\n",
    "        if upper_bound < 1e9:\n",
    "                print()\n",
    "                confidence_old=confidence\n",
    "                confidence = (lower_bound + upper_bound)/2\n",
    "    else:\n",
    "        lower_bound = max(lower_bound,confidence)\n",
    "        confidence_old=confidence\n",
    "        if upper_bound < 1e9:\n",
    "                confidence = (lower_bound + upper_bound)/2\n",
    "        else:\n",
    "                confidence *= 10\n",
    "                \n",
    "    print(\"outer_step={} confidence {}->{}\".format(outer_step,confidence_old,confidence))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-28T05:01:49.529287Z",
     "start_time": "2019-11-28T05:01:49.522491Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 3, 224, 224)\n",
      "(1, 3, 224, 224)\n"
     ]
    }
   ],
   "source": [
    "print(o_bestattack.shape)\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-28T05:01:50.926167Z",
     "start_time": "2019-11-28T05:01:50.621807Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 224, 224)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "adv=o_bestattack[0]\n",
    "print(adv.shape)\n",
    "adv = adv.transpose(1, 2, 0)\n",
    "adv = (adv * std) + mean\n",
    "adv = adv * 255.0\n",
    "adv = np.clip(adv, 0, 255).astype(np.uint8)\n",
    "\n",
    "show_images_diff(orig,0,adv,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
